Abstract
Deep Learning (DL) has shown real promise for the classification efficiency for emotion recognition problems. In this paper we present experimental results for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven emotions. The first architecture explores the impact of reducing the number of deep learning layers and the second splits the input images horizontally into two streams based on eye and mouth positions. The first proposed architecture produces state of the art results with an accuracy rate of 96.93 % and the second architecture with split input produces an average accuracy rate of 86.73 %, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lewis, M., Haviland-Jones, J., Barrett, L.: Handbook of Emotions. Guilford Press, New York (2008)
Chavhan, A., Chavan, S., Dahe, S., Chibhade, S.: A neural network approach for real time emotion recognition. IJARCCE 4(3), 259–263 (2015)
Han, K., Yu, D., Tashev, I.: Speech emotion recognition using deep neural network and extreme learning machine. In: Interspeech, pp. 223–227 (2014)
Cohen, I., Garg, A., Huang, T.: Emotion recognition from facial expressions using multi-level HMM. In: Neural Information Processing Systems, vol. 2 (2000)
Sarnarawickrame, K., Mindya, S.: Facial expression recognition using active shape models and support vector machines. In: 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 51–55 (2013)
Boughrara, H., Chtourou, M., Ben Amar, C., Chen, L.: Facial expression recognition based on a mlp neural network using constructive training algorithm. Multimed. Tools Appl. 75, 709–731 (2014)
Kahou, S., Michalski, V., Konda, K., Memisevic, R., Pal, C.: Recurrent neural networks for emotion recognition in video. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI 2015), pp. 467–474 (2015)
Levi, G., Hassner, T.: Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI 2015), pp. 503–510 (2015)
Ouellet, S.: Realtime emotion recognition for gaming using deep convolutional network features. CoRR. abs/1408.3750 (2014)
Szegedy, C., Lui, W., Jia, Y., Sermanet, P., Reed, S., Auguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–19 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1106–1114 (2012)
Burkert, P., Trier, F., Afzal, M.Z., Dengel, A., Liwicki, M.: DeXpression: Deep Convolutional Neural Network for Expression Recognition. CoRR. abs/1509.05371 (2015)
Lawrence, S., Giles, C., Tsoi, A.C., Back, A.: Face recognition: a convolutional neural network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997)
Brosch, T., Tam, R.: Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images. Neural Computation. 27, 211–227 (2015)
Altahhan, A.: Navigating a robot through big visual sensory data. Procedia Comput. Sci. 53, 478–485 (2015)
Khashman, A.: Application of an emotional neural network to facial recognition. Neural Comput. Appl. 18, 309–320 (2008)
Sohail, A., Bhattacharya, P.: Classifying facial expressions using level set method based lip contour detection and multi-class support vector machines. Int. J. Pattern Recogn. Artif. Intell. 25, 835–862 (2011)
Hewahi, N., Baraka, A.: Impact of ethnic group on human emotion recognition using backpropagation neural network. Broad Res. Artif. Intell. Neurosci. 2, 20–27 (2011)
Ahsan, T., Jabid, T., Chong, U.: Facial expression recognition using local transitional pattern on gabor filtered facial images. IETE Tech Rev. 30, 47 (2013)
Chelali, F., Djeradi, A.: Face recognition using MLP and RBF neural network with Gabor and discrete wavelet transform characterization: a comparative study. Math. Prob. Eng. 2015, 116 (2015)
Lundqvist, D., Flykt, A., Ahman, A.: The Karolinska Directed Emotional Faces - KDEF. CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet (1998). ISBN 91-630-7164-9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ruiz-Garcia, A., Elshaw, M., Altahhan, A., Palade, V. (2016). Deep Learning for Emotion Recognition in Faces. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)